The present invention relates to an associative pattern conversion system and adaptation method thereof.
Conventionally, pattern-matching was applied to character or acoustic recognition. On such recognition, it has been impossible to judge an input-signal exactly unless there is high coincidence between the input signal and the registered pattern.
On the other hand, various models of neural network based on a neuron model has been proposed. One neuron model was announced by W. S. McCloch and W. H. Pitts in Massachusetts Institute of Technology in U.S.A. in 1943. It was then proved that associative pattern-matching is possible by using neural network by F. Rosenblatt.
However, it is impossible to realize a neural network with an integrated circuit; that is, it is impossible to realize the circuit with practical possibility, using all of present technology of semiconductors because an enormous number of output pins are necessary to output the associative pattern.
For example, when a character is input as a binary pattern of 32.times.32 dots, 10.sup.6 (=(32.sup.2).sup.2) neurons are necessary to process the input as an orthogonal data and 10.sup.6 output pins are necessary to output the ignition pattern of all these neurons.
There was some attempts to realize neuron model by software or hardware.
When the neuron model is realized by software, enormous memory is spent and the process speed is far from practical use.
When the neuron model is realized by hardware, the system is not amendable to slight modifications of the neuron model due to its lack of flexibility. The neuron model usually had to be simplified because of the difficulty in electronically regenerating the neuron model in a strict meaning.